There are various types of interference and noise sources in a multi-carrier communication system, such as a Discrete Multiple Tone (DMT) system. Interference and noise may corrupt the data-bearing signal on a tone as the signal travels through the communication channel and is decoded at the receiver. The transmitted data-bearing signal may be decoded erroneously by the receiver because of this signal corruption. The number of data bits or the amount of information that a tone carries may vary from tone to tone and depends on the relative power of the data-bearing signal compared to the power of the corrupting signal on that particular tone.
In order to account for potential interference on the transmission line and to guarantee a reliable communication between the transmitter and receiver, each tone of a DMT system is typically designed to carry a limited number of data bits per unit time based on the tone's Signal to Noise Ratio (SNR) using a bit-loading algorithm, which is an algorithm to determine the number of bits per tone. The number of bits that a specific tone may carry decreases as the relative strength of the corrupting signal increases, that is when the SNR is low or the bit error rate (BER) is high. Thus, the SNR of a tone may be used to determine how much data should be transmitted by the tone at a target bit error rate.
It is often assumed that the corrupting signal is an additive random source with Gaussian distribution and white spectrum. With this assumption, the number of data bits that each tone can carry relates directly to the SNR. However, this assumption may not be true in many practical cases and there are various sources of interference that do not have a white, Gaussian distribution. Impulse noise is one such noise source. Bit-loading algorithms are usually designed based on the assumption of additive, white, Gaussian noise. With such algorithms, the effects of Impulse Noise can be underestimated resulting in an excessive rate of error.
Further, channel estimation procedures that can be designed to optimize performance in the presence of stationary impairments such as additive, white, Gaussian noise, but are often poor at estimating non-stationary or cyclo-stationary impairments, such as impulse noise. Consequently, a Digital Subscriber Line (DSL) modem training procedures are typically well suited to optimizing performance in the presence of additive, white, Gaussian noise, but leave the modem receivers relatively defenseless to impulse noise.